• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.233-233
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• 2022

As the world's population grows and food needs diversify, the demand for horticultural crops for beneficial traits is increasing. In order to meet this demand, it is necessary to develop suitable cultivars and breeding methods accordingly. Breeding methods have changed over time. With the recent development of sequencing technology, the concept of genomic selection (GS) has emerged as large-scale genome information can be used. GS shows good predictive ability even for quantitative traits by using various markers, breaking away from the limitations of Marker Assisted Selection (MAS). Moreover, GS using machine learning (ML) and deep learning (DL) has been studied recently. In this study, we aim to build a system that selects phenotype-related markers using the genomic information of the pepper population and trains a genomic selection model to select individuals from the validation population. We plan to establish an optimal genome wide association analysis model by comparing and analyzing five models. Validation of molecular markers by applying linkage markers discovered through genome wide association analysis to breeding populations. Finally, we plan to establish an optimal genome selection model by comparing and analyzing 12 genome selection models. Then We will use the genome selection model of the learning group in the breeding group to verify the prediction accuracy and discover a prediction model.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.11-22
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• 2023

In this paper, we propose an efficient satellite selection method for multi-constellation GNSS. The number of visible satellites has increased dramatically recently due to multi-constellation GNSS. By the increased availability, the overall GNSS performance can be improved. Whereas, due to the increase of the number of visible satellites, the computational burden in implementing advanced processing such as integer ambiguity resolution and fault detection can be increased considerably. As widely known, the optimal satellite selection method requires very large computational burden and its real-time implementation is practically impossible. To reduce computational burden, several sub-optimal but efficient satellite selection methods have been proposed recently. However, these methods are prone to the local optimum problem and do not fully utilize the information redundancy between different constellation systems. To solve this problem, the proposed method utilizes the inter-system biases and geometric assignments. As a result, the proposed method can be implemented in real-time, avoids the local optimum problem, and does not exclude any single-satellite constellation. The performance of the proposed method is compared with the optimal method and two popular sub-optimal methods by a simulation and an experiment.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.579-590
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• 2012

Local linear jump detection in a discontinuous regression function involves the choice of the bandwidth and the performance of a local linear jump detector depends heavily on the choice of the bandwidth. However, little attention has been paid to this important issue. In this paper we propose two fully data adaptive bandwidth selection methods for a local linear jump detector. The performance of the proposed methods are investigated through a simulation study.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.143-155
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• 2009

Best subset selection procedure based on mutual information (MI) between a set of explanatory variables and a dependent class variable is suggested. Derivation of multivariate MI is based on normal mixtures. Several types of normal mixtures are proposed. Also a best subset selection algorithm is proposed. Four real data sets are employed to demonstrate the efficiency of the proposals.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.553-562
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• 2004

In this short communication we discuss the bias problems of CART in split variable selection and suggest a method to reduce the variable selection bias. Penalties proportional to the number of categories or distinct values are applied to the splitting criteria of CART. The results of empirical comparisons show that the proposed modification of CART reduces the bias in variable selection.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1397-1409
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• 2017

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

• Animal Bioscience
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• v.35 no.6
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• pp.804-813
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• 2022

Objective: A formula is needed that is practical for current livestock breeding methods and that predicts the approximate rate of inbreeding (ΔF) in populations where selection is performed according to four-path programs (sires to breed sons, sires to breed daughters, dams to breed sons, and dams to breed daughters). The formula widely used to predict inbreeding neglects selection, we need to develop a new formula that can be applied with or without selection. Methods: The core of the prediction is to incorporate the long-tern genetic influence of the selected parents in four-selection paths executed as sires to breed sons, sires to breed daughters, dams to breed sons, and dams to breed daughters. The rate of inbreeding was computed as the magnitude that is proportional to the sum of squared long-term genetic contributions of the parents of four-selection paths to the selected offspring. Results: We developed a formula to predict the rate of inbreeding in populations undergoing four-path selection on genomically enhanced breeding values and with discrete generations. The new formula can be applied with or without selection. Neglecting the effects of selection led to underestimation of the rate of inbreeding by 40% to 45%. Conclusion: The formula we developed here would be highly useful as a practical method for predicting the approximate rate of inbreeding (ΔF) in populations where selection is performed according to four-path programs.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.453-458
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• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• Journal of the Korea Institute of Building Construction
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• v.15 no.4
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• pp.405-412
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• 2015

The procurement of public construction, which has been selected mainly on the basis of the work experience of a department of public agencies that award construction contracts or those in charge of ordering, has not been executed through an objective selection process considering the construction types or characteristics. Thus, as a preliminary study on developing a model for the selection of contract methods, this study intends to analyze and present the key factors affecting the selection of procurement methods in public construction. Through a literature review on the existing methods, foundational factors were first presented, based on the analysis of these factors, the final influence factors were proposed. As a result, 6 factors contractor's characteristics, the environment of the construction market, relevant organization, the characteristics of a project, costs, and responsibility factors were determined, and 14 sub-factors were selected. The factors presented in this study will be used as base data for developing a decision making support model for the selection of contract methods in public construction.

• Journal of The Korean Association For Science Education
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• v.13 no.2
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• pp.172-186
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• 1993

This stady was carried out to define Gifted student for science, model for selection, the tools and methods and related theory for the selection of the Gifted students for the science in primary school level. Also the developed tools and materials are applied to student and analysed the results to generalize the methods for the selection of Gifted students for science. The definition of Gifted students for science was carried out by the three-ring conception model by Renzulli(1982) and Lee long-Sung which defined the characteristics as three parts such as above average ability, creativity and tesk comitment. The Gifted students for science upper 2 percent which have three characteristics at the same times, namely overlapping three characteristics. The model for the selection of Gifted students consist of four step; such as screeing, selection,differentiation, judgement. The materials for the selection are input at each stage, analysed the results and standard for the selection are made. In the first stage screening, 202 students are selected from the 5060 of 4th and 5th graders according to their achievment, intellecture ability and observation of students activity. In second selection and third differentiation stage, 65 students are seletted according to their achievement In this study it is approved that the Gifted students in science have to be selection by various test such as achievement, intellectual ability, aptitude in science, inquiry activity, manual skill etc, rather rather then simple test such as achievement and intellecture ability. Also it is important to select upper 2 percent who have general abilites overlapping three characteristics mentioned in definition of Gifted students in science and selections model